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<a href="_l_m_c_m_a_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment"> * \brief       -</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> *</span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> * \author      Thomas Voss and Christian Igel</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> * \date        April 2014</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> *</span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> * </span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> * </span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published </span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * </span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * </span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> *</span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> */</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="preprocessor">#ifndef SHARK_ALGORITHMS_DIRECT_SEARCH_LM_CMA_H</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="preprocessor">#define SHARK_ALGORITHMS_DIRECT_SEARCH_LM_CMA_H</span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span> </div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_single_objective_optimizer_8h.html">shark/Algorithms/AbstractSingleObjectiveOptimizer.h</a>&gt;</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="preprocessor">#include &lt;<a class="code" href="_multi_variate_normal_distribution_8h.html">shark/Statistics/Distributions/MultiVariateNormalDistribution.h</a>&gt;</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span><span class="preprocessor">#include &lt;<a class="code" href="_individual_8h.html">shark/Algorithms/DirectSearch/Individual.h</a>&gt;</span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span> </div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span><span class="preprocessor">#include &lt;<a class="code" href="_penalizing_evaluator_8h.html">shark/Algorithms/DirectSearch/Operators/Evaluation/PenalizingEvaluator.h</a>&gt;</span></div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span><span class="preprocessor">#include &lt;<a class="code" href="_population_based_step_size_adaptation_8h.html">shark/Algorithms/DirectSearch/Operators/PopulationBasedStepSizeAdaptation.h</a>&gt;</span></div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span><span class="preprocessor">#include &lt;<a class="code" href="_elitist_selection_8h.html">shark/Algorithms/DirectSearch/Operators/Selection/ElitistSelection.h</a>&gt;</span></div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span> </div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span> </div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span> </div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a> {</div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span> </div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span><span class="keyword">namespace </span>detail{</div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span><span class="comment"></span> </div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="comment">///\brief Approximates a Limited Memory Cholesky Matrix from a stream of samples.</span></div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span><span class="comment">///</span></div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span><span class="comment">/// Given a set of points \f$ v_i\f$, produces an approximation of the cholesky factor of a matrix:</span></div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="comment">/// \f[ AA^T=C= (1-\alpha) C^{t-1} + \alpha* x_{j_t} x_{j_t}^T \f]</span></div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span><span class="comment">/// here the \f$ j_t \f$ are chosen such to have an approximate distance \f$ N_{steps} \f$. It is assumed</span></div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span><span class="comment">/// that the \f$x_i \f$ are correlated and thus a big \f$ N_{steps} \f$ tris to get points which are less </span></div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span><span class="comment">/// correlated. The matrix keeps a set of vectors and decides at every step which is will discard.</span></div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span><span class="comment">///</span></div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span><span class="comment">/// This is the corrected algorithm as proposed in </span></div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span><span class="comment">/// Ilya Loshchilov, &quot;A Computationally Efficient Limited Memory CMA-ES for Large Scale Optimization&quot;</span></div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span><span class="comment">/// \ingroup singledirect</span></div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span><span class="comment"></span><span class="keyword">class </span>IncrementalCholeskyMatrix{</div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span>    IncrementalCholeskyMatrix(){}</div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span>    <span class="keywordtype">void</span> init (<span class="keywordtype">double</span> alpha,std::size_t dimensions, std::size_t numVectors, std::size_t Nsteps){</div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span>        m_vArr.resize(numVectors,dimensions);</div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span>        m_pcArr.resize(numVectors,dimensions);</div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span>        m_b.resize(numVectors);</div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span>        m_d.resize(numVectors);</div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span>        m_l.resize(numVectors);</div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span>        m_j.resize(0);<span class="comment">//nothing stored at the bginning</span></div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span>        m_Nsteps = Nsteps;</div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span>        m_maxStoredVectors = numVectors;</div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span>        m_counter = 0;</div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span>        m_alpha = alpha;</div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span>        </div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span>        m_vArr.clear();</div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span>        m_pcArr.clear();</div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span>        m_b.clear();</div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span>        m_d.clear();</div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span>        m_l.clear();</div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span>    }</div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span> </div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span>    <span class="comment">//computes x = Az</span></div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> T&gt;</div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span>    <span class="keywordtype">void</span> prod(RealVector&amp; x, T <span class="keyword">const</span>&amp; z)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span>        x = z;</div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span>        <span class="keywordtype">double</span> a = std::sqrt(1-m_alpha);</div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span>        <span class="keywordflow">for</span>(std::size_t j=0; j != m_j.size(); j++){</div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span>            std::size_t jcur = m_j[j];  </div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>            <span class="keywordtype">double</span> k = m_b(jcur) *inner_prod(row(m_vArr,jcur),z);</div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>            noalias(x) = a*x+k*row(m_pcArr,jcur);</div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span>        }</div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>    }</div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span>    </div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>    <span class="comment">//computes x= A^{-1}z</span></div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> T&gt;</div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>    <span class="keywordtype">void</span> inv(RealVector&amp; x, T <span class="keyword">const</span>&amp; z)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>        inv(x,z,m_j.size());</div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>    }</div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>    </div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span>    <span class="keywordtype">void</span> update(RealVector <span class="keyword">const</span>&amp; newPc){</div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span>        std::size_t imin = 0;<span class="comment">//the index of the removed point</span></div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span>        <span class="keywordflow">if</span> (m_j.size() &lt; m_maxStoredVectors)</div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span>        {</div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span>            std::size_t index = m_j.size();</div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span>            m_j.push_back(index);</div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>            imin = index;</div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>        }</div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>        <span class="keywordflow">else</span></div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span>        {</div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span>            <span class="comment">//find the largest &quot;age&quot;gap between neighbouring points (i.e. the time between insertion)</span></div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span>            <span class="comment">//we want to remove the smallest gap as to make the</span></div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span>            <span class="comment">//time distances as equal as possible</span></div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>            std::size_t dmin = m_l[m_j[1]] - m_l[m_j[0]];</div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>            imin = 1;</div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>            <span class="keywordflow">for</span>(std::size_t j=2; j != m_j.size(); j++)</div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span>            {</div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>                std::size_t dcur = m_l[m_j[j]] - m_l[m_j[j-1]];</div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span>                <span class="keywordflow">if</span> (dcur &lt; dmin)</div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>                {</div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>                    dmin = dcur;</div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>                    imin = j;</div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>                }</div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>            }</div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span>            <span class="comment">//if the gap is bigger than Nsteps, we remove the oldest point to</span></div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>            <span class="comment">//shrink it.</span></div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>            <span class="keywordflow">if</span> (dmin &gt;= m_Nsteps)</div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>                imin = 0;</div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span>            <span class="comment">//we push all points backwards and append the freed index to the end of the list</span></div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span>            <span class="keywordflow">if</span> (imin != m_j.size()-1)</div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span>            {</div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>                std::size_t sav = m_j[imin];</div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>                <span class="keywordflow">for</span>(std::size_t j = imin; j != m_j.size()-1; j++)</div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span>                    m_j[j] = m_j[j+1];</div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span>                m_j.back() = sav;</div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span>            }</div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span>        }</div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>        <span class="comment">//set the values of the new added index</span></div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>        <span class="keywordtype">int</span> newidx = m_j.back();</div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>        m_l[newidx] = m_counter;</div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>        noalias(row(m_pcArr,newidx)) = newPc;</div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span>        ++m_counter;</div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>    </div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>        <span class="comment">// this procedure recomputes v vectors correctly, in the original LM-CMA-ES they were outdated/corrupted.</span></div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>        <span class="comment">// all vectors v_k,v_{k+1},...,v_m are corrupted where k=j_imin. it also computes the proper v and b/d values for the newest</span></div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>        <span class="comment">// inserted vector</span></div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>        RealVector v;</div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>        <span class="keywordflow">for</span>(std::size_t i = imin; i != m_j.size(); ++i)</div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>        {</div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>            <span class="keywordtype">int</span> index = m_j[i];</div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span>            inv(v,row(m_pcArr,index),i);</div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span>            noalias(row(m_vArr,index)) = v;</div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span> </div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>            <span class="keywordtype">double</span> normv2 = norm_sqr(row(m_vArr,index));</div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>            <span class="keywordtype">double</span> c = std::sqrt(1.0-m_alpha);</div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>            <span class="keywordtype">double</span> f = std::sqrt(1+m_alpha/(1-m_alpha)*normv2);</div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>            m_b[index] = c/normv2*(f-1);</div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>            m_d[index] = 1/(c*normv2)*(1-1/f);</div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>        }</div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>    }</div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>    </div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> T&gt;</div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>    <span class="keywordtype">void</span> inv(RealVector&amp; x, T <span class="keyword">const</span>&amp; z,std::size_t k)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>        x = z;</div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>        <span class="keywordtype">double</span> c= 1.0/std::sqrt(1-m_alpha);</div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span>        <span class="keywordflow">for</span>(std::size_t j=0; j != k; j++){<span class="comment">// O(m*n)</span></div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span>            std::size_t jcur = m_j[j];</div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span>            <span class="keywordtype">double</span> k = m_d(jcur) * inner_prod(row(m_vArr,jcur),x);</div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span>            noalias(x) = c*x - k*row(m_vArr,jcur);</div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span>        }</div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span>    }</div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span> </div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span>    <span class="comment">//variables making up A</span></div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span>    RealMatrix m_vArr;</div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span>    RealMatrix m_pcArr;</div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span>    RealVector m_b;</div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span>    RealVector m_d;</div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span>    </div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span>    <span class="comment">//index variables for computation of A</span></div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span>    std::vector&lt;std::size_t&gt; m_j;</div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span>    std::vector&lt;std::size_t&gt; m_l;</div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span>    std::size_t m_Nsteps;</div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span>    std::size_t m_maxStoredVectors;</div>
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span>    std::size_t m_counter;</div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span>    </div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span>    <span class="keywordtype">double</span> m_alpha;</div>
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span>};</div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span>}</div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span><span class="comment"></span> </div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span><span class="comment">/// \brief Implements a Limited-Memory-CMA</span></div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span><span class="comment">///</span></div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span><span class="comment">/// This is the algorithm as proposed in </span></div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span><span class="comment">/// Ilya Loshchilov, &quot;A Computationally Efficient Limited Memory CMA-ES for Large Scale Optimization&quot;</span></div>
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span><span class="comment">/// with a few corrections regarding the covariance matrix update.</span></div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span><span class="comment">///</span></div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span><span class="comment">/// The algorithm stores a subset of previous evolution path vectors and approximates the covariance</span></div>
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span><span class="comment">/// matrix based on this. This algorithm only requires O(nm) memory, where n is the dimensionality</span></div>
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span><span class="comment">/// and n the problem dimensionality. To be more exact, 2*m vectors of size n are stored to calculate</span></div>
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno">  194</span><span class="comment">/// the matrix-vector product with the choelsky factor of the covariance matrix in O(mn). </span></div>
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span><span class="comment">///</span></div>
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span><span class="comment">/// The algorithm uses the population based step size adaptation strategy as proposed in</span></div>
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno">  197</span><span class="comment">/// the same paper.</span></div>
<div class="foldopen" id="foldopen00198" data-start="{" data-end="};">
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno"><a class="line" href="classshark_1_1_l_m_c_m_a.html">  198</a></span><span class="comment"></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_l_m_c_m_a.html" title="Implements a Limited-Memory-CMA.">LMCMA</a>: <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_single_objective_optimizer.html" title="Base class for all single objective optimizer.">AbstractSingleObjectiveOptimizer</a>&lt;RealVector &gt;</div>
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno">  199</span>{</div>
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno">  200</span><span class="keyword">public</span>:<span class="comment"></span></div>
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno">  201</span><span class="comment">    /// \brief Default c&#39;tor.</span></div>
<div class="foldopen" id="foldopen00202" data-start="{" data-end="}">
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno"><a class="line" href="classshark_1_1_l_m_c_m_a.html#a1f2852ce27c7333c44630c5294246c42">  202</a></span><span class="comment"></span>    <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a1f2852ce27c7333c44630c5294246c42" title="Default c&#39;tor.">LMCMA</a>(random::rng_type&amp; rng = <a class="code hl_variable" href="namespaceshark_1_1random.html#ab5c1547eee483974d008d43f621a2234">random::globalRng</a>):mpe_rng(&amp;rng){</div>
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno">  203</span>        <a class="code hl_variable" href="classshark_1_1_abstract_optimizer.html#a72daf583d406e144b90869f311baa594">m_features</a> |= <a class="code hl_enumvalue" href="classshark_1_1_abstract_optimizer.html#a77bf437afee3445601c680cc652410f0af46b9e1111a0858df3670fe12e4ffbf0">REQUIRES_VALUE</a>;</div>
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno">  204</span>    }</div>
</div>
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno">  205</span>    </div>
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno">  206</span>    <span class="comment"></span></div>
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno">  207</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00208" data-start="{" data-end="}">
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno"><a class="line" href="classshark_1_1_l_m_c_m_a.html#a758588cfe6ceb146bc54375c257333d8">  208</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a758588cfe6ceb146bc54375c257333d8" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno">  209</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;LMCMA-ES&quot;</span>; }</div>
</div>
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno">  210</span><span class="comment"></span> </div>
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno">  211</span><span class="comment">    /// \brief Calculates lambda for the supplied dimensionality n.</span></div>
<div class="foldopen" id="foldopen00212" data-start="{" data-end="}">
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno"><a class="line" href="classshark_1_1_l_m_c_m_a.html#a8846e369c997015e1759d7ce315a3563">  212</a></span><span class="comment"></span>    <span class="keywordtype">unsigned</span> <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a8846e369c997015e1759d7ce315a3563" title="Calculates lambda for the supplied dimensionality n.">suggestLambda</a>( <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimension ) {</div>
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno">  213</span>        <span class="keywordtype">unsigned</span> <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a0937e25d91951e5e8f10923ab5da5340" title="Returns a immutable reference to the size of the offspring population .">lambda</a> = unsigned( 4. + ::floor( 3. * ::log( <span class="keyword">static_cast&lt;</span><span class="keywordtype">double</span><span class="keyword">&gt;</span>( dimension ) ) ) );</div>
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno">  214</span>        <span class="comment">// heuristic for small search spaces</span></div>
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno">  215</span>        <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a0937e25d91951e5e8f10923ab5da5340" title="Returns a immutable reference to the size of the offspring population .">lambda</a> = std::max&lt;unsigned int&gt;( 5, std::min( <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a0937e25d91951e5e8f10923ab5da5340" title="Returns a immutable reference to the size of the offspring population .">lambda</a>, dimension ) );</div>
<div class="line"><a id="l00216" name="l00216"></a><span class="lineno">  216</span>        <span class="keywordflow">return</span>( <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a0937e25d91951e5e8f10923ab5da5340" title="Returns a immutable reference to the size of the offspring population .">lambda</a> );</div>
<div class="line"><a id="l00217" name="l00217"></a><span class="lineno">  217</span>    }</div>
</div>
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno">  218</span><span class="comment"></span> </div>
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno">  219</span><span class="comment">    /// \brief Calculates mu for the supplied lambda and the recombination strategy.</span></div>
<div class="foldopen" id="foldopen00220" data-start="{" data-end="}">
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno"><a class="line" href="classshark_1_1_l_m_c_m_a.html#aa9751cb33edf7452cee4984f1c5fb2e7">  220</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#aa9751cb33edf7452cee4984f1c5fb2e7" title="Calculates mu for the supplied lambda and the recombination strategy.">suggestMu</a>( <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a0937e25d91951e5e8f10923ab5da5340" title="Returns a immutable reference to the size of the offspring population .">lambda</a>) {</div>
<div class="line"><a id="l00221" name="l00221"></a><span class="lineno">  221</span>        <span class="keywordflow">return</span> <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a0937e25d91951e5e8f10923ab5da5340" title="Returns a immutable reference to the size of the offspring population .">lambda</a> / 2.;</div>
<div class="line"><a id="l00222" name="l00222"></a><span class="lineno">  222</span>    }</div>
</div>
<div class="line"><a id="l00223" name="l00223"></a><span class="lineno">  223</span> </div>
<div class="line"><a id="l00224" name="l00224"></a><span class="lineno">  224</span>    <span class="keyword">using </span><a class="code hl_class" href="classshark_1_1_abstract_single_objective_optimizer.html" title="Base class for all single objective optimizer.">AbstractSingleObjectiveOptimizer</a>&lt;RealVector &gt;<a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a0636affdccf6eca66033fa85424ae7c4" title="Initializes the algorithm for the supplied objective function.">::init</a>;</div>
<div class="line"><a id="l00225" name="l00225"></a><span class="lineno">  225</span>    <span class="comment"></span></div>
<div class="line"><a id="l00226" name="l00226"></a><span class="lineno">  226</span><span class="comment">    /// \brief Initializes the algorithm for the supplied objective function.</span></div>
<div class="foldopen" id="foldopen00227" data-start="{" data-end="}">
<div class="line"><a id="l00227" name="l00227"></a><span class="lineno"><a class="line" href="classshark_1_1_l_m_c_m_a.html#a0636affdccf6eca66033fa85424ae7c4">  227</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a0636affdccf6eca66033fa85424ae7c4" title="Initializes the algorithm for the supplied objective function.">init</a>( <a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#aa4c05609c54d7ebc99d099e7dd6e228f">ObjectiveFunctionType</a> <span class="keyword">const</span>&amp; function, <a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#a85f0d04fdfb094dba4dc80b1fb5e3adb">SearchPointType</a> <span class="keyword">const</span>&amp; p) {</div>
<div class="line"><a id="l00228" name="l00228"></a><span class="lineno">  228</span>        <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a0937e25d91951e5e8f10923ab5da5340" title="Returns a immutable reference to the size of the offspring population .">lambda</a> = <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a8846e369c997015e1759d7ce315a3563" title="Calculates lambda for the supplied dimensionality n.">suggestLambda</a>( p.size() );</div>
<div class="line"><a id="l00229" name="l00229"></a><span class="lineno">  229</span>        <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a185f5f8fa9efd945008d6492f9ad5ae4" title="Returns the size of the parent population .">mu</a> = <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#aa9751cb33edf7452cee4984f1c5fb2e7" title="Calculates mu for the supplied lambda and the recombination strategy.">suggestMu</a>(  <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a0937e25d91951e5e8f10923ab5da5340" title="Returns a immutable reference to the size of the offspring population .">lambda</a> );</div>
<div class="line"><a id="l00230" name="l00230"></a><span class="lineno">  230</span>        <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a0636affdccf6eca66033fa85424ae7c4" title="Initializes the algorithm for the supplied objective function.">init</a>( function,</div>
<div class="line"><a id="l00231" name="l00231"></a><span class="lineno">  231</span>            p,</div>
<div class="line"><a id="l00232" name="l00232"></a><span class="lineno">  232</span>            <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a0937e25d91951e5e8f10923ab5da5340" title="Returns a immutable reference to the size of the offspring population .">lambda</a>,</div>
<div class="line"><a id="l00233" name="l00233"></a><span class="lineno">  233</span>            <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a185f5f8fa9efd945008d6492f9ad5ae4" title="Returns the size of the parent population .">mu</a>,</div>
<div class="line"><a id="l00234" name="l00234"></a><span class="lineno">  234</span>            1.0/std::sqrt(<span class="keywordtype">double</span>(p.size()))</div>
<div class="line"><a id="l00235" name="l00235"></a><span class="lineno">  235</span>        );</div>
<div class="line"><a id="l00236" name="l00236"></a><span class="lineno">  236</span>    }</div>
</div>
<div class="line"><a id="l00237" name="l00237"></a><span class="lineno">  237</span><span class="comment"></span> </div>
<div class="line"><a id="l00238" name="l00238"></a><span class="lineno">  238</span><span class="comment">    /// \brief Initializes the algorithm for the supplied objective function.</span></div>
<div class="foldopen" id="foldopen00239" data-start="{" data-end="}">
<div class="line"><a id="l00239" name="l00239"></a><span class="lineno"><a class="line" href="classshark_1_1_l_m_c_m_a.html#a7371b088ce92161da051ce664a97e9a0">  239</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a7371b088ce92161da051ce664a97e9a0" title="Initializes the algorithm for the supplied objective function.">init</a>( </div>
<div class="line"><a id="l00240" name="l00240"></a><span class="lineno">  240</span>        <a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#aa4c05609c54d7ebc99d099e7dd6e228f">ObjectiveFunctionType</a> <span class="keyword">const</span>&amp; function, </div>
<div class="line"><a id="l00241" name="l00241"></a><span class="lineno">  241</span>        <a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#a85f0d04fdfb094dba4dc80b1fb5e3adb">SearchPointType</a> <span class="keyword">const</span>&amp; initialSearchPoint,</div>
<div class="line"><a id="l00242" name="l00242"></a><span class="lineno">  242</span>        <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a0937e25d91951e5e8f10923ab5da5340" title="Returns a immutable reference to the size of the offspring population .">lambda</a>, </div>
<div class="line"><a id="l00243" name="l00243"></a><span class="lineno">  243</span>        <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a185f5f8fa9efd945008d6492f9ad5ae4" title="Returns the size of the parent population .">mu</a>,</div>
<div class="line"><a id="l00244" name="l00244"></a><span class="lineno">  244</span>        <span class="keywordtype">double</span> initialSigma</div>
<div class="line"><a id="l00245" name="l00245"></a><span class="lineno">  245</span>    ) {</div>
<div class="line"><a id="l00246" name="l00246"></a><span class="lineno">  246</span>        <a class="code hl_function" href="classshark_1_1_abstract_optimizer.html#ae7a23300641448c761b6aa0305b7ef66" title="Convenience function that checks whether the features of the supplied objective function match with t...">checkFeatures</a>(function);</div>
<div class="line"><a id="l00247" name="l00247"></a><span class="lineno">  247</span>        </div>
<div class="line"><a id="l00248" name="l00248"></a><span class="lineno">  248</span>        m_numberOfVariables = function.numberOfVariables();</div>
<div class="line"><a id="l00249" name="l00249"></a><span class="lineno">  249</span>        m_lambda = <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a0937e25d91951e5e8f10923ab5da5340" title="Returns a immutable reference to the size of the offspring population .">lambda</a>;</div>
<div class="line"><a id="l00250" name="l00250"></a><span class="lineno">  250</span>        m_mu = <span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span>(::floor(<a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a185f5f8fa9efd945008d6492f9ad5ae4" title="Returns the size of the parent population .">mu</a>));</div>
<div class="line"><a id="l00251" name="l00251"></a><span class="lineno">  251</span> </div>
<div class="line"><a id="l00252" name="l00252"></a><span class="lineno">  252</span>        <span class="comment">//set initial point</span></div>
<div class="line"><a id="l00253" name="l00253"></a><span class="lineno">  253</span>        m_mean = initialSearchPoint;</div>
<div class="line"><a id="l00254" name="l00254"></a><span class="lineno">  254</span>        <a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.point = initialSearchPoint;</div>
<div class="line"><a id="l00255" name="l00255"></a><span class="lineno">  255</span>        <a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.value = function(initialSearchPoint);</div>
<div class="line"><a id="l00256" name="l00256"></a><span class="lineno">  256</span>        </div>
<div class="line"><a id="l00257" name="l00257"></a><span class="lineno">  257</span>        <span class="comment">//init step size adaptation</span></div>
<div class="line"><a id="l00258" name="l00258"></a><span class="lineno">  258</span>        m_stepSize.<a class="code hl_function" href="classshark_1_1_population_based_step_size_adaptation.html#a83113ac7f0853d0e7e8fc679816efa6e" title="Initializes a new trial by setting the initial learning rate and resetting the internal values.">init</a>(initialSigma);</div>
<div class="line"><a id="l00259" name="l00259"></a><span class="lineno">  259</span>            </div>
<div class="line"><a id="l00260" name="l00260"></a><span class="lineno">  260</span>        <span class="comment">//weighting of the mu-best individuals</span></div>
<div class="line"><a id="l00261" name="l00261"></a><span class="lineno">  261</span>        m_weights.resize(m_mu);</div>
<div class="line"><a id="l00262" name="l00262"></a><span class="lineno">  262</span>        <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; m_mu; i++){</div>
<div class="line"><a id="l00263" name="l00263"></a><span class="lineno">  263</span>            m_weights(i) = ::log(<a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a185f5f8fa9efd945008d6492f9ad5ae4" title="Returns the size of the parent population .">mu</a> + 0.5) - ::log(1. + i);</div>
<div class="line"><a id="l00264" name="l00264"></a><span class="lineno">  264</span>        }</div>
<div class="line"><a id="l00265" name="l00265"></a><span class="lineno">  265</span>        m_weights /= sum(m_weights);</div>
<div class="line"><a id="l00266" name="l00266"></a><span class="lineno">  266</span>        </div>
<div class="line"><a id="l00267" name="l00267"></a><span class="lineno">  267</span>        <span class="comment">// learning rates</span></div>
<div class="line"><a id="l00268" name="l00268"></a><span class="lineno">  268</span>        m_muEff = 1. / sum(<a class="code hl_function" href="group__shark__globals.html#gae1f82613484173e9fe1a07960dabff63" title="Calculates x^2.">sqr</a>(m_weights)); <span class="comment">// equal to sum(m_weights)^2 / sum(sqr(m_weights))</span></div>
<div class="line"><a id="l00269" name="l00269"></a><span class="lineno">  269</span>        <span class="keywordtype">double</span> c1 = 1/(10*std::log(m_numberOfVariables+1.0));</div>
<div class="line"><a id="l00270" name="l00270"></a><span class="lineno">  270</span>        m_cC =1.0/m_lambda;</div>
<div class="line"><a id="l00271" name="l00271"></a><span class="lineno">  271</span>        </div>
<div class="line"><a id="l00272" name="l00272"></a><span class="lineno">  272</span>        <span class="comment">//init variables for covariance matrix update</span></div>
<div class="line"><a id="l00273" name="l00273"></a><span class="lineno">  273</span>        m_evolutionPathC = blas::repeat(0.0,m_numberOfVariables);</div>
<div class="line"><a id="l00274" name="l00274"></a><span class="lineno">  274</span>        m_A.init(c1,m_numberOfVariables,<a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a0937e25d91951e5e8f10923ab5da5340" title="Returns a immutable reference to the size of the offspring population .">lambda</a>,<a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a0937e25d91951e5e8f10923ab5da5340" title="Returns a immutable reference to the size of the offspring population .">lambda</a>);</div>
<div class="line"><a id="l00275" name="l00275"></a><span class="lineno">  275</span>    }</div>
</div>
<div class="line"><a id="l00276" name="l00276"></a><span class="lineno">  276</span><span class="comment"></span> </div>
<div class="line"><a id="l00277" name="l00277"></a><span class="lineno">  277</span><span class="comment">    /// \brief Executes one iteration of the algorithm.</span></div>
<div class="foldopen" id="foldopen00278" data-start="{" data-end="}">
<div class="line"><a id="l00278" name="l00278"></a><span class="lineno"><a class="line" href="classshark_1_1_l_m_c_m_a.html#a9e0f9265cf2592e3c88f85418457d190">  278</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a9e0f9265cf2592e3c88f85418457d190" title="Executes one iteration of the algorithm.">step</a>(<a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#aa4c05609c54d7ebc99d099e7dd6e228f">ObjectiveFunctionType</a> <span class="keyword">const</span>&amp; function){</div>
<div class="line"><a id="l00279" name="l00279"></a><span class="lineno">  279</span>        <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_individual.html" title="Individual is a simple templated class modelling an individual that acts as a candidate solution in a...">Individual&lt;RealVector, double, RealVector&gt;</a> <a class="code hl_typedef" href="_t_s_p_8cpp.html#aa2af735cda68526094633853c8a9894d">IndividualType</a>;</div>
<div class="line"><a id="l00280" name="l00280"></a><span class="lineno">  280</span>        std::vector&lt; IndividualType &gt; offspring( m_lambda );</div>
<div class="line"><a id="l00281" name="l00281"></a><span class="lineno">  281</span> </div>
<div class="line"><a id="l00282" name="l00282"></a><span class="lineno">  282</span>        <a class="code hl_struct" href="structshark_1_1_penalizing_evaluator.html" title="Penalizing evaluator for scalar objective functions.">PenalizingEvaluator</a> penalizingEvaluator;</div>
<div class="line"><a id="l00283" name="l00283"></a><span class="lineno">  283</span>        <span class="keywordflow">for</span>( <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; offspring.size(); i++ ) {</div>
<div class="line"><a id="l00284" name="l00284"></a><span class="lineno">  284</span>            createSample(offspring[i].<a class="code hl_function" href="namespaceshark.html#a68954303294e98c77d03dad52e32bd9e">searchPoint</a>(),offspring[i].chromosome());</div>
<div class="line"><a id="l00285" name="l00285"></a><span class="lineno">  285</span>        }</div>
<div class="line"><a id="l00286" name="l00286"></a><span class="lineno">  286</span>        penalizingEvaluator( function, offspring.begin(), offspring.end() );</div>
<div class="line"><a id="l00287" name="l00287"></a><span class="lineno">  287</span> </div>
<div class="line"><a id="l00288" name="l00288"></a><span class="lineno">  288</span>        <span class="comment">// Selection and parameter update</span></div>
<div class="line"><a id="l00289" name="l00289"></a><span class="lineno">  289</span>        <span class="comment">// opposed to normal CMA selection, we don&#39;t remove any indidivudals but only order</span></div>
<div class="line"><a id="l00290" name="l00290"></a><span class="lineno">  290</span>        <span class="comment">// them by rank to allow the use of the population based strategy.</span></div>
<div class="line"><a id="l00291" name="l00291"></a><span class="lineno">  291</span>        std::vector&lt; IndividualType &gt; parents( <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a0937e25d91951e5e8f10923ab5da5340" title="Returns a immutable reference to the size of the offspring population .">lambda</a>() );</div>
<div class="line"><a id="l00292" name="l00292"></a><span class="lineno">  292</span>        <a class="code hl_struct" href="structshark_1_1_elitist_selection.html" title="Survival selection to find the next parent set.">ElitistSelection&lt; IndividualType::FitnessOrdering &gt;</a> selection;</div>
<div class="line"><a id="l00293" name="l00293"></a><span class="lineno">  293</span>        selection(offspring.begin(),offspring.end(),parents.begin(), parents.end());</div>
<div class="line"><a id="l00294" name="l00294"></a><span class="lineno">  294</span>        updateStrategyParameters( parents );</div>
<div class="line"><a id="l00295" name="l00295"></a><span class="lineno">  295</span> </div>
<div class="line"><a id="l00296" name="l00296"></a><span class="lineno">  296</span>        <span class="comment">//update the best solution found so far.</span></div>
<div class="line"><a id="l00297" name="l00297"></a><span class="lineno">  297</span>        <a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.point= parents[ 0 ].searchPoint();</div>
<div class="line"><a id="l00298" name="l00298"></a><span class="lineno">  298</span>        <a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.value= parents[ 0 ].unpenalizedFitness();</div>
<div class="line"><a id="l00299" name="l00299"></a><span class="lineno">  299</span>    }</div>
</div>
<div class="line"><a id="l00300" name="l00300"></a><span class="lineno">  300</span><span class="comment"></span> </div>
<div class="line"><a id="l00301" name="l00301"></a><span class="lineno">  301</span><span class="comment">    /// \brief Accesses the current step size.</span></div>
<div class="foldopen" id="foldopen00302" data-start="{" data-end="}">
<div class="line"><a id="l00302" name="l00302"></a><span class="lineno"><a class="line" href="classshark_1_1_l_m_c_m_a.html#a226a579df53c6b4d28d854912999f667">  302</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a226a579df53c6b4d28d854912999f667" title="Accesses the current step size.">sigma</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00303" name="l00303"></a><span class="lineno">  303</span>        <span class="keywordflow">return</span> m_stepSize.<a class="code hl_function" href="classshark_1_1_population_based_step_size_adaptation.html#a7b9e246e1aea24500b766d2deecab993">stepSize</a>();</div>
<div class="line"><a id="l00304" name="l00304"></a><span class="lineno">  304</span>    }</div>
</div>
<div class="line"><a id="l00305" name="l00305"></a><span class="lineno">  305</span><span class="comment"></span> </div>
<div class="line"><a id="l00306" name="l00306"></a><span class="lineno">  306</span><span class="comment">    /// \brief Accesses the current population mean.</span></div>
<div class="foldopen" id="foldopen00307" data-start="{" data-end="}">
<div class="line"><a id="l00307" name="l00307"></a><span class="lineno"><a class="line" href="classshark_1_1_l_m_c_m_a.html#af03c5c09687c82760dd2c4f94ef4adc4">  307</a></span><span class="comment"></span>    RealVector <span class="keyword">const</span>&amp; <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#af03c5c09687c82760dd2c4f94ef4adc4" title="Accesses the current population mean.">mean</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00308" name="l00308"></a><span class="lineno">  308</span>        <span class="keywordflow">return</span> m_mean;</div>
<div class="line"><a id="l00309" name="l00309"></a><span class="lineno">  309</span>    }</div>
</div>
<div class="line"><a id="l00310" name="l00310"></a><span class="lineno">  310</span><span class="comment"></span> </div>
<div class="line"><a id="l00311" name="l00311"></a><span class="lineno">  311</span><span class="comment">    /// \brief Accesses the current weighting vector.</span></div>
<div class="foldopen" id="foldopen00312" data-start="{" data-end="}">
<div class="line"><a id="l00312" name="l00312"></a><span class="lineno"><a class="line" href="classshark_1_1_l_m_c_m_a.html#a9986354da96031a9ea68893836293075">  312</a></span><span class="comment"></span>    RealVector <span class="keyword">const</span>&amp; <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a9986354da96031a9ea68893836293075" title="Accesses the current weighting vector.">weights</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00313" name="l00313"></a><span class="lineno">  313</span>        <span class="keywordflow">return</span> m_weights;</div>
<div class="line"><a id="l00314" name="l00314"></a><span class="lineno">  314</span>    }</div>
</div>
<div class="line"><a id="l00315" name="l00315"></a><span class="lineno">  315</span><span class="comment"></span> </div>
<div class="line"><a id="l00316" name="l00316"></a><span class="lineno">  316</span><span class="comment">    /// \brief Accesses the evolution path for the covariance matrix update.</span></div>
<div class="foldopen" id="foldopen00317" data-start="{" data-end="}">
<div class="line"><a id="l00317" name="l00317"></a><span class="lineno"><a class="line" href="classshark_1_1_l_m_c_m_a.html#aaab9999a4247ecfef8caa3d26045545a">  317</a></span><span class="comment"></span>    RealVector <span class="keyword">const</span>&amp; <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#aaab9999a4247ecfef8caa3d26045545a" title="Accesses the evolution path for the covariance matrix update.">evolutionPath</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00318" name="l00318"></a><span class="lineno">  318</span>        <span class="keywordflow">return</span> m_evolutionPathC;</div>
<div class="line"><a id="l00319" name="l00319"></a><span class="lineno">  319</span>    }</div>
</div>
<div class="line"><a id="l00320" name="l00320"></a><span class="lineno">  320</span>    <span class="comment"></span></div>
<div class="line"><a id="l00321" name="l00321"></a><span class="lineno">  321</span><span class="comment">    /// \brief Returns the size of the parent population \f$\mu\f$.</span></div>
<div class="foldopen" id="foldopen00322" data-start="{" data-end="}">
<div class="line"><a id="l00322" name="l00322"></a><span class="lineno"><a class="line" href="classshark_1_1_l_m_c_m_a.html#a185f5f8fa9efd945008d6492f9ad5ae4">  322</a></span><span class="comment"></span>    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a185f5f8fa9efd945008d6492f9ad5ae4" title="Returns the size of the parent population .">mu</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00323" name="l00323"></a><span class="lineno">  323</span>        <span class="keywordflow">return</span> m_mu;</div>
<div class="line"><a id="l00324" name="l00324"></a><span class="lineno">  324</span>    }</div>
</div>
<div class="line"><a id="l00325" name="l00325"></a><span class="lineno">  325</span>    <span class="comment"></span></div>
<div class="line"><a id="l00326" name="l00326"></a><span class="lineno">  326</span><span class="comment">    /// \brief Returns a mutabl rference to the size of the parent population \f$\mu\f$.</span></div>
<div class="foldopen" id="foldopen00327" data-start="{" data-end="}">
<div class="line"><a id="l00327" name="l00327"></a><span class="lineno"><a class="line" href="classshark_1_1_l_m_c_m_a.html#a7a022e8fa7e293afc0bea7dce439d464">  327</a></span><span class="comment"></span>    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&amp; <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a7a022e8fa7e293afc0bea7dce439d464" title="Returns a mutabl rference to the size of the parent population .">mu</a>(){</div>
<div class="line"><a id="l00328" name="l00328"></a><span class="lineno">  328</span>        <span class="keywordflow">return</span> m_mu;</div>
<div class="line"><a id="l00329" name="l00329"></a><span class="lineno">  329</span>    }</div>
</div>
<div class="line"><a id="l00330" name="l00330"></a><span class="lineno">  330</span>    <span class="comment"></span></div>
<div class="line"><a id="l00331" name="l00331"></a><span class="lineno">  331</span><span class="comment">    /// \brief Returns a immutable reference to the size of the offspring population \f$\mu\f$.</span></div>
<div class="foldopen" id="foldopen00332" data-start="{" data-end="}">
<div class="line"><a id="l00332" name="l00332"></a><span class="lineno"><a class="line" href="classshark_1_1_l_m_c_m_a.html#a0937e25d91951e5e8f10923ab5da5340">  332</a></span><span class="comment"></span>    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a0937e25d91951e5e8f10923ab5da5340" title="Returns a immutable reference to the size of the offspring population .">lambda</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00333" name="l00333"></a><span class="lineno">  333</span>        <span class="keywordflow">return</span> m_lambda;</div>
<div class="line"><a id="l00334" name="l00334"></a><span class="lineno">  334</span>    }</div>
</div>
<div class="line"><a id="l00335" name="l00335"></a><span class="lineno">  335</span><span class="comment"></span> </div>
<div class="line"><a id="l00336" name="l00336"></a><span class="lineno">  336</span><span class="comment">    /// \brief Returns a mutable reference to the size of the offspring population \f$\mu\f$.</span></div>
<div class="foldopen" id="foldopen00337" data-start="{" data-end="}">
<div class="line"><a id="l00337" name="l00337"></a><span class="lineno"><a class="line" href="classshark_1_1_l_m_c_m_a.html#aae01a84a110e8b1d0d091e306e64ebe4">  337</a></span><span class="comment"></span>    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> &amp; <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#aae01a84a110e8b1d0d091e306e64ebe4" title="Returns a mutable reference to the size of the offspring population .">lambda</a>(){</div>
<div class="line"><a id="l00338" name="l00338"></a><span class="lineno">  338</span>        <span class="keywordflow">return</span> m_lambda;</div>
<div class="line"><a id="l00339" name="l00339"></a><span class="lineno">  339</span>    }</div>
</div>
<div class="line"><a id="l00340" name="l00340"></a><span class="lineno">  340</span> </div>
<div class="line"><a id="l00341" name="l00341"></a><span class="lineno">  341</span><span class="keyword">private</span>:<span class="comment"></span></div>
<div class="line"><a id="l00342" name="l00342"></a><span class="lineno">  342</span><span class="comment">    /// \brief Updates the strategy parameters based on the supplied offspring population.</span></div>
<div class="line"><a id="l00343" name="l00343"></a><span class="lineno">  343</span><span class="comment"></span>    <span class="keywordtype">void</span> updateStrategyParameters( std::vector&lt;<a class="code hl_class" href="classshark_1_1_individual.html" title="Individual is a simple templated class modelling an individual that acts as a candidate solution in a...">Individual&lt;RealVector, double, RealVector&gt;</a> &gt; <span class="keyword">const</span>&amp; offspring ) {</div>
<div class="line"><a id="l00344" name="l00344"></a><span class="lineno">  344</span>        <span class="comment">//line 8, creation of the new mean (but not updating the mean of the distribution</span></div>
<div class="line"><a id="l00345" name="l00345"></a><span class="lineno">  345</span>        RealVector m( m_numberOfVariables, 0. );</div>
<div class="line"><a id="l00346" name="l00346"></a><span class="lineno">  346</span>        <span class="keywordflow">for</span>( <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> j = 0; j &lt; <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a185f5f8fa9efd945008d6492f9ad5ae4" title="Returns the size of the parent population .">mu</a>(); j++ ){</div>
<div class="line"><a id="l00347" name="l00347"></a><span class="lineno">  347</span>            noalias(m) += m_weights( j ) * offspring[j].searchPoint();</div>
<div class="line"><a id="l00348" name="l00348"></a><span class="lineno">  348</span>        }</div>
<div class="line"><a id="l00349" name="l00349"></a><span class="lineno">  349</span>        </div>
<div class="line"><a id="l00350" name="l00350"></a><span class="lineno">  350</span>        <span class="comment">//update evolution path, line 9</span></div>
<div class="line"><a id="l00351" name="l00351"></a><span class="lineno">  351</span>        noalias(m_evolutionPathC) = (1. - m_cC ) * m_evolutionPathC + std::sqrt( m_cC * (2. - m_cC) * m_muEff ) * (m - m_mean) / <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a226a579df53c6b4d28d854912999f667" title="Accesses the current step size.">sigma</a>();</div>
<div class="line"><a id="l00352" name="l00352"></a><span class="lineno">  352</span>        </div>
<div class="line"><a id="l00353" name="l00353"></a><span class="lineno">  353</span>        <span class="comment">//update mean now, as oldmean is not needed any more (line 8 continued)</span></div>
<div class="line"><a id="l00354" name="l00354"></a><span class="lineno">  354</span>        m_mean = m;</div>
<div class="line"><a id="l00355" name="l00355"></a><span class="lineno">  355</span>        </div>
<div class="line"><a id="l00356" name="l00356"></a><span class="lineno">  356</span>        <span class="comment">//corrected version of lines 10-14- the covariance matrix adaptation</span></div>
<div class="line"><a id="l00357" name="l00357"></a><span class="lineno">  357</span>        <span class="comment">//we replace one vector that makes up the approximation of A by the newly updated evolution path</span></div>
<div class="line"><a id="l00358" name="l00358"></a><span class="lineno">  358</span>        m_A.update(m_evolutionPathC);</div>
<div class="line"><a id="l00359" name="l00359"></a><span class="lineno">  359</span>        </div>
<div class="line"><a id="l00360" name="l00360"></a><span class="lineno">  360</span>        <span class="comment">//update the step size using the population success rule, line 15-18</span></div>
<div class="line"><a id="l00361" name="l00361"></a><span class="lineno">  361</span>        m_stepSize.<a class="code hl_function" href="classshark_1_1_population_based_step_size_adaptation.html#a9f4e464bb4dfbf1dee52898069b6570c" title="updates the step size using the newly sampled population">update</a>(offspring);</div>
<div class="line"><a id="l00362" name="l00362"></a><span class="lineno">  362</span> </div>
<div class="line"><a id="l00363" name="l00363"></a><span class="lineno">  363</span>    }</div>
<div class="line"><a id="l00364" name="l00364"></a><span class="lineno">  364</span>    <span class="comment"></span></div>
<div class="line"><a id="l00365" name="l00365"></a><span class="lineno">  365</span><span class="comment">    /// \brief Creates a vector-sample pair x=Az, where z is a gaussian random vector.</span></div>
<div class="line"><a id="l00366" name="l00366"></a><span class="lineno">  366</span><span class="comment"></span>    <span class="keywordtype">void</span> createSample(RealVector&amp; x,RealVector&amp; z)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00367" name="l00367"></a><span class="lineno">  367</span>        x.resize(m_numberOfVariables);</div>
<div class="line"><a id="l00368" name="l00368"></a><span class="lineno">  368</span>        z.resize(m_numberOfVariables);</div>
<div class="line"><a id="l00369" name="l00369"></a><span class="lineno">  369</span>        <span class="keywordflow">for</span>(std::size_t i = 0; i != m_numberOfVariables; ++i){</div>
<div class="line"><a id="l00370" name="l00370"></a><span class="lineno">  370</span>            z(i) = <a class="code hl_function" href="namespaceshark_1_1random.html#a972c5f7f031612a130aa077fc9136a9f" title="Draws a number from the normal distribution with given mean and variance by drawing random numbers fr...">gauss</a>(*mpe_rng,0,1);</div>
<div class="line"><a id="l00371" name="l00371"></a><span class="lineno">  371</span>        }</div>
<div class="line"><a id="l00372" name="l00372"></a><span class="lineno">  372</span>        m_A.prod(x,z);</div>
<div class="line"><a id="l00373" name="l00373"></a><span class="lineno">  373</span>        noalias(x) = <a class="code hl_function" href="classshark_1_1_l_m_c_m_a.html#a226a579df53c6b4d28d854912999f667" title="Accesses the current step size.">sigma</a>()*x +m_mean;</div>
<div class="line"><a id="l00374" name="l00374"></a><span class="lineno">  374</span>    }</div>
<div class="line"><a id="l00375" name="l00375"></a><span class="lineno">  375</span>    </div>
<div class="line"><a id="l00376" name="l00376"></a><span class="lineno">  376</span>    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> m_numberOfVariables; <span class="comment">///&lt; Stores the dimensionality of the search space.</span></div>
<div class="line"><a id="l00377" name="l00377"></a><span class="lineno">  377</span>    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> m_mu; <span class="comment">///&lt; The size of the parent population.</span></div>
<div class="line"><a id="l00378" name="l00378"></a><span class="lineno">  378</span>    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> m_lambda; <span class="comment">///&lt; The size of the offspring population, needs to be larger than mu.</span></div>
<div class="line"><a id="l00379" name="l00379"></a><span class="lineno">  379</span> </div>
<div class="line"><a id="l00380" name="l00380"></a><span class="lineno">  380</span>    <span class="keywordtype">double</span> m_cC;<span class="comment">///&lt; learning rate of the evolution path</span></div>
<div class="line"><a id="l00381" name="l00381"></a><span class="lineno">  381</span>    </div>
<div class="line"><a id="l00382" name="l00382"></a><span class="lineno">  382</span> </div>
<div class="line"><a id="l00383" name="l00383"></a><span class="lineno">  383</span>    detail::IncrementalCholeskyMatrix m_A;</div>
<div class="line"><a id="l00384" name="l00384"></a><span class="lineno">  384</span>    PopulationBasedStepSizeAdaptation m_stepSize;<span class="comment">///&lt; step size adaptation for the step size sigma()</span></div>
<div class="line"><a id="l00385" name="l00385"></a><span class="lineno">  385</span>    </div>
<div class="line"><a id="l00386" name="l00386"></a><span class="lineno">  386</span>    RealVector m_mean; <span class="comment">///&lt; current mean of the distribution</span></div>
<div class="line"><a id="l00387" name="l00387"></a><span class="lineno">  387</span>    RealVector m_weights;<span class="comment">///&lt; weighting for the mu best individuals</span></div>
<div class="line"><a id="l00388" name="l00388"></a><span class="lineno">  388</span>    <span class="keywordtype">double</span> m_muEff;<span class="comment">///&lt; effective sample size for the weighted samples</span></div>
<div class="line"><a id="l00389" name="l00389"></a><span class="lineno">  389</span> </div>
<div class="line"><a id="l00390" name="l00390"></a><span class="lineno">  390</span>    RealVector m_evolutionPathC;<span class="comment">///&lt; evolution path</span></div>
<div class="line"><a id="l00391" name="l00391"></a><span class="lineno">  391</span>    </div>
<div class="line"><a id="l00392" name="l00392"></a><span class="lineno">  392</span>    random::rng_type* mpe_rng;</div>
<div class="line"><a id="l00393" name="l00393"></a><span class="lineno">  393</span>    </div>
<div class="line"><a id="l00394" name="l00394"></a><span class="lineno">  394</span>};</div>
</div>
<div class="line"><a id="l00395" name="l00395"></a><span class="lineno">  395</span> </div>
<div class="line"><a id="l00396" name="l00396"></a><span class="lineno">  396</span>}</div>
<div class="line"><a id="l00397" name="l00397"></a><span class="lineno">  397</span> </div>
<div class="line"><a id="l00398" name="l00398"></a><span class="lineno">  398</span><span class="preprocessor">#endif</span></div>
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